We initialize the network by first training on a synthetic dataset generated using the method followed by [1]}. The training set consists of 1.5 million word crops with equal distribution for all the scripts, which are randomly sampled to form minibatches of size 96. Further, the network is fine-tuned on the benchmark datasets and evaluated. We adopt some data augmentation techniques such as varying contrast, adding noise and perspective transformations to make the dataset robust. The Adam optimizer is used with the initial learning rate being set to 0.001 and is automatically adjusted by the optimizer during training. The training of the network on Nvidia Geforce GTX 1080ti GPU, 16GB RAM takes about 36 hours for 10 epochs. The network has approximately 1.1 million parameters and takes 2.7 ms per image for identification.
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